http://www.biomedcentral.com/bmcbioinformatics/ The most accessed research articles published by BMC Bioinformatics 2009-11-18T00:00:00Z This is an RSS newsfeed from BioMed Central It is intended to be used with an RSS reader. For more information about RSS newsfeeds from BioMed Central, visit http://www.biomedcentral.com/info/about/rss/ Background: The remarkable advance of metagenomics presents significant new challenges in data analysis. Metagenomic datasets (metagenomes) are large collections of sequencing reads from anonymous species within particular environments. Computational analyses for very large metagenomes are extremely time-consuming, and there are often many novel sequences in these metagenomes that are not fully utilized. The number of available metagenomes is rapidly increasing, so fast and efficient metagenome comparison methods are in great demand. Results: The new metagenomic data analysis method Rapid Analysis of Multiple Metagenomes with a Clustering and Annotation Pipeline (RAMMCAP) was developed using an ultra-fast sequence clustering algorithm, fast protein family annotation tools, and a novel statistical metagenome comparison method that employs a unique graphic interface. RAMMCAP processes extremely large datasets with only moderate computational effort. It identifies raw read clusters and protein clusters that may include novel gene families, and compares metagenomes using clusters or functional annotations calculated by RAMMCAP. In this study, RAMMCAP was applied to the two largest available metagenomic collections, the "Global Ocean Sampling" and the "Metagenomic Profiling of Nine Biomes". Conclusion: RAMMCAP is a very fast method that can cluster and annotate one million metagenomic reads in only hundreds of CPU hours. It is available from http://tools.camera.calit2.net/camera/rammcap/. http://www.biomedcentral.com/1471-2105/10/359 Weizhong Li BMC Bioinformatics 2009, 10:359 2009-10-28T00:00:00Z doi:10.1186/1471-2105-10-359 BMC Bioinformatics 1471-2105 10 359 2009-10-28T00:00:00Z XML Background: Evolutionary trees are central to a wide range of biological studies. In many of these studies, tree nodes and branches need to be associated (or annotated) with various attributes. For example, in studies concerned with organismal relationships, tree nodes are associated with taxonomic names, whereas tree branches have lengths and oftentimes support values. Gene trees used in comparative genomics or phylogenomics are usually annotated with taxonomic information, genome-related data, such as gene names and functional annotations, as well as events such as gene duplications, speciations, or exon shufflings, combined with information related to the evolutionary tree itself. The data standards currently used for evolutionary trees have limited capacities to incorporate such annotations of different data types. Results: We developed a XML language, named phyloXML, for describing evolutionary trees, as well as various associated data items. PhyloXML provides elements for commonly used items, such as branch lengths, support values, taxonomic names, and gene names and identifiers. By using "property" elements, phyloXML can be adapted to novel and unforeseen use cases. We also developed various software tools for reading, writing, conversion, and visualization of phyloXML formatted data. Conclusion: PhyloXML is an XML language defined by a complete schema in XSD that allows storing and exchanging the structures of evolutionary trees as well as associated data. More information about phyloXML itself, the XSD schema, as well as tools implementing and supporting phyloXML, is available at http://www.phyloxml.org. http://www.biomedcentral.com/1471-2105/10/356 Mira Han Christian Zmasek BMC Bioinformatics 2009, 10:356 2009-10-27T00:00:00Z doi:10.1186/1471-2105-10-356 BMC Bioinformatics 1471-2105 10 356 2009-10-27T00:00:00Z XML Background: Biological networks are widely used to represent processes in biological systems and to capture interactions and dependencies between biological entities. Their size and complexity is steadily increasing due to the ongoing growth of knowledge in the life sciences. To aid understanding of biological networks several algorithms for laying out and graphically representing networks and network analysis results have been developed. However, current algorithms are specialized to particular layout styles and therefore different algorithms are required for each kind of network and/or style of layout. This increases implementation effort and means that new algorithms must be developed for new layout styles. Furthermore, additional effort is necessary to compose different layout conventions in the same diagram. Also the user cannot usually customize the placement of nodes to tailor the layout to their particular need or task and there is little support for interactive network exploration. Results: We present a novel algorithm to visualize different biological networks and network analysis results in meaningful ways depending on network types and analysis outcome. Our method is based on constrained graph layout and we demonstrate how it can handle the drawing conventions used in biological networks. Conclusions: The presented algorithm offers the ability to produce many of the fundamental popular drawing styles while allowing the flexibility of constraints to further tailor these layouts. http://www.biomedcentral.com/1471-2105/10/375 Falk Schreiber Tim Dwyer Kim Marriott Michael Wybrow BMC Bioinformatics 2009, 10:375 2009-11-12T00:00:00Z doi:10.1186/1471-2105-10-375 BMC Bioinformatics 1471-2105 10 375 2009-11-12T00:00:00Z PDF Background: Even though real-time PCR has been broadly applied in biomedical sciences, data processing procedures for the analysis of quantitative real-time PCR are still lacking; specifically in the realm of appropriate statistical treatment. Confidence interval and statistical significance considerations are not explicit in many of the current data analysis approaches. Based on the standard curve method and other useful data analysis methods, we present and compare four statistical approaches and models for the analysis of real-time PCR data. Results: In the first approach, a multiple regression analysis model was developed to derive ΔΔCt from estimation of interaction of gene and treatment effects. In the second approach, an ANCOVA (analysis of covariance) model was proposed, and the ΔΔCt can be derived from analysis of effects of variables. The other two models involve calculation ΔCt followed by a two group t-test and non-parametric analogous Wilcoxon test. SAS programs were developed for all four models and data output for analysis of a sample set are presented. In addition, a data quality control model was developed and implemented using SAS. Conclusion: Practical statistical solutions with SAS programs were developed for real-time PCR data and a sample dataset was analyzed with the SAS programs. The analysis using the various models and programs yielded similar results. Data quality control and analysis procedures presented here provide statistical elements for the estimation of the relative expression of genes using real-time PCR. http://www.biomedcentral.com/1471-2105/7/85 Joshua Yuan Ann Reed Feng Chen C. Stewart BMC Bioinformatics 2006, 7:85 2006-02-22T00:00:00Z doi:10.1186/1471-2105-7-85 BMC Bioinformatics 1471-2105 7 85 2006-02-22T00:00:00Z XML Background: A great deal of data has accumulated on signalling pathways. These large datasets are thought to contain much implicit information on their molecular structure, interaction and activity information, which provides a picture of intricate molecular networks believed to underlie biological functions. While tremendous advances have been made in trying to understand these systems, how information is transmitted within them is still poorly understood. This ever growing amount of data demands we adopt powerful computational techniques that will play a pivotal role in the conversion of mined data to knowledge, and in elucidating the topological and functional properties of protein - protein interactions. Results: A computational framework is presented which allows for the description of embedded networks, and identification of common shared components thought to assist in the transmission of information within the systems studied. By employing the graph theories of network biology - such as degree distribution, clustering coefficient, vertex betweenness and shortest path measures - topological features of protein-protein interactions for published datasets of the p53, nuclear factor kappa B (NF-κB) and G1/S phase of the cell cycle systems were ascertained. Highly ranked nodes which in some cases were identified as connecting proteins most likely responsible for propagation of transduction signals across the networks were determined. The functional consequences of these nodes in the context of their network environment were also determined. These findings highlight the usefulness of the framework in identifying possible combination or links as targets for therapeutic responses; and put forward the idea of using retrieved knowledge on the shared components in constructing better organised and structured models of signalling networks. Conclusion: It is hoped that through the data mined reconstructed signal transduction networks, well developed models of the published data can be built which in the end would guide the prediction of new targets based on the pathway's environment for further analysis. Source code is available upon request. http://www.biomedcentral.com/1471-2105/10/370 Adaoha Ihekwaba Phuong Nguyen Corrado Priami BMC Bioinformatics 2009, 10:370 2009-11-06T00:00:00Z doi:10.1186/1471-2105-10-370 BMC Bioinformatics 1471-2105 10 370 2009-11-06T00:00:00Z XML Background: Natural History science is characterised by a single immense goal (to document, describe and synthesise all facets pertaining to the diversity of life) that can only be addressed through a seemingly infinite series of smaller studies. The discipline's failure to meaningfully connect these small studies with natural history's goal has made it hard to demonstrate the value of natural history to a wider scientific community. Digital technologies provide the means to bridge this gap. Results: We describe the system architecture and template design of "Scratchpads", a data-publishing framework for groups of people to create their own social networks supporting natural history science. Scratchpads cater to the particular needs of individual research communities through a common database and system architecture. This is flexible and scalable enough to support multiple networks, each with its own choice of features, visual design, and constituent data. Our data model supports web services on standardised data elements that might be used by related initiatives such as GBIF and the Encyclopedia of Life. A Scratchpad allows users to organise data around user-defined or imported ontologies, including biological classifications. Automated semantic annotation and indexing is applied to all content, allowing users to navigate intuitively and curate diverse biological data, including content drawn from third party resources. A system of archiving citable pages allows stable referencing with unique identifiers and provides credit to contributors through normal citation processes. Conclusion: Our framework http://scratchpads.eu/ currently serves more than 1,100 registered users across 100 sites, spanning academic, amateur and citizen-science audiences. These users have generated more than 130,000 nodes of content in the first two years of use. The template of our architecture may serve as a model to other research communities developing data publishing frameworks outside biodiversity research. http://www.biomedcentral.com/1471-2105/10/S14/S6 BMC Bioinformatics 2009, 10:S6 2009-11-10T00:00:00Z doi:10.1186/1471-2105-10-S14-S6 BMC Bioinformatics 1471-2105 10 S6 2009-11-10T00:00:00Z XML Background: The regulation of many cell functions is inherently linked to cell-cell contact interactions. However, effects of contact interactions among adherent cells can be difficult to detect with global summary statistics due to the localized nature and noise inherent to cell-cell interactions. The lack of informatics approaches specific for detecting cell-cell interactions is a limitation in the analysis of large sets of cell image data, including traditional and combinatorial or high-throughput studies. Here we introduce a novel histogram-based data analysis strategy, termed local cell metrics (LCMs), which addresses this shortcoming. Results: The new LCM method is demonstrated via a study of contact inhibition of proliferation of MC3T3-E1 osteoblasts. We describe how LCMs can be used to quantify the local environment of cells and how LCMs are decomposed mathematically into metrics specific to each cell type in a culture, e.g., differently-labelled cells in fluorescence imaging. Using this approach, a quantitative, probabilistic description of the contact inhibition effects in MC3T3-E1 cultures has been achieved. We also show how LCMs are related to the naïve Bayes model. Namely, LCMs are Bayes class-conditional probability functions, suggesting their use for data mining and classification. Conclusion: LCMs are successful in robust detection of cell contact inhibition in situations where conventional global statistics fail to do so. The noise due to the random features of cell behavior was suppressed significantly as a result of the focus on local distances, providing sensitive detection of cell-cell contact effects. The methodology can be extended to any quantifiable feature that can be obtained from imaging of cell cultures or tissue samples, including optical, fluorescent, and confocal microscopy. This approach may prove useful in interpreting culture and histological data in fields where cell-cell interactions play a critical role in determining cell fate, e.g., cancer, developmental biology, and tissue regeneration. http://www.biomedcentral.com/1471-2105/10/350 Jing Su Pedro Zapata Chien-Chiang Chen J. Carson Meredith BMC Bioinformatics 2009, 10:350 2009-10-23T00:00:00Z doi:10.1186/1471-2105-10-350 BMC Bioinformatics 1471-2105 10 350 2009-10-23T00:00:00Z XML Background: A fundamental goal of the U.S. National Institute of Health (NIH) "Roadmap" is to strengthen Translational Research, defined as the movement of discoveries in basic research to application at the clinical level. A significant barrier to translational research is the lack of uniformly structured data across related biomedical domains. The Semantic Web is an extension of the current Web that enables navigation and meaningful use of digital resources by automatic processes. It is based on common formats that support aggregation and integration of data drawn from diverse sources. A variety of technologies have been built on this foundation that, together, support identifying, representing, and reasoning across a wide range of biomedical data. The Semantic Web Health Care and Life Sciences Interest Group (HCLSIG), set up within the framework of the World Wide Web Consortium, was launched to explore the application of these technologies in a variety of areas. Subgroups focus on making biomedical data available in RDF, working with biomedical ontologies, prototyping clinical decision support systems, working on drug safety and efficacy communication, and supporting disease researchers navigating and annotating the large amount of potentially relevant literature. Results: We present a scenario that shows the value of the information environment the Semantic Web can support for aiding neuroscience researchers. We then report on several projects by members of the HCLSIG, in the process illustrating the range of Semantic Web technologies that have applications in areas of biomedicine. Conclusion: Semantic Web technologies present both promise and challenges. Current tools and standards are already adequate to implement components of the bench-to-bedside vision. On the other hand, these technologies are young. Gaps in standards and implementations still exist and adoption is limited by typical problems with early technology, such as the need for a critical mass of practitioners and installed base, and growing pains as the technology is scaled up. Still, the potential of interoperable knowledge sources for biomedicine, at the scale of the World Wide Web, merits continued work. http://www.biomedcentral.com/1471-2105/8/S3/S2 BMC Bioinformatics 2007, 8:S2 2007-05-09T00:00:00Z doi:10.1186/1471-2105-8-S3-S2 BMC Bioinformatics 1471-2105 8 S2 2007-05-09T00:00:00Z XML Background: A number of tools for the examination of linkage disequilibrium (LD) patterns between nearby alleles exist, but none are available for quickly and easily investigating LD at longer ranges (>500 kb). We have developed a web-based query tool (GLIDERS: Genome-wide LInkage DisEquilibrium Repository and Search engine) that enables the retrieval of pairwise associations with r2 ≥ 0.3 across the human genome for any SNP genotyped within HapMap phase 2 and 3, regardless of distance between the markers.DescriptionGLIDERS is an easy to use web tool that only requires the user to enter rs numbers of SNPs they want to retrieve genome-wide LD for (both nearby and long-range). The intuitive web interface handles both manual entry of SNP IDs as well as allowing users to upload files of SNP IDs. The user can limit the resulting inter SNP associations with easy to use menu options. These include MAF limit (5-45%), distance limits between SNPs (minimum and maximum), r2 (0.3 to 1), HapMap population sample (CEU, YRI and JPT+CHB combined) and HapMap build/release. All resulting genome-wide inter-SNP associations are displayed on a single output page, which has a link to a downloadable tab delimited text file. Conclusion: GLIDERS is a quick and easy way to retrieve genome-wide inter-SNP associations and to explore LD patterns for any number of SNPs of interest. GLIDERS can be useful in identifying SNPs with long-range LD. This can highlight mis-mapping or other potential association signal localisation problems. http://www.biomedcentral.com/1471-2105/10/367 Robert Lawrence Aaron Day-Williams Richard Mott John Broxholme Lon Cardon Eleftheria Zeggini BMC Bioinformatics 2009, 10:367 2009-10-31T00:00:00Z doi:10.1186/1471-2105-10-367 BMC Bioinformatics 1471-2105 10 367 2009-10-31T00:00:00Z XML Background: Biological sequences play a major role in molecular and computational biology. They are studied as information-bearing entities that make up DNA, RNA or proteins. The Sequence Ontology, which is part of the OBO Foundry, contains descriptions and definitions of sequences and their properties. Yet the most basic question about sequences remains unanswered: what kind of entity is a biological sequence? An answer to this question benefits formal ontologies that use the notion of biological sequences and analyses in computational biology alike. Results: We provide both an ontological analysis of biological sequences and a formal representation that can be used in knowledge-based applications and other ontologies. We distinguish three distinct kinds of entities that can be referred to as ``biological sequence'': chains of molecules, syntactic representations such as those in biological databases, and the abstract information-bearing entities. For use in knowledge-based applications and inclusion in biomedical ontologies, we implemented the developed axiom system for use in automated theorem proving. Conclusions: Axioms are necessary to achieve the main goal of ontologies: to formally specify the meaning of terms used within a domain. The axiom system for the ontology of biological sequences is the first elaborate axiom system for an OBO Foundry ontology and can serve as starting point for the development of more formal ontologies and ultimately of knowledge-based applications. http://www.biomedcentral.com/1471-2105/10/377 Robert Hoehndorf Janet Kelso Heinrich Herre BMC Bioinformatics 2009, 10:377 2009-11-18T00:00:00Z doi:10.1186/1471-2105-10-377 BMC Bioinformatics 1471-2105 10 377 2009-11-18T00:00:00Z PDF